Deep Learning–Based Localization and Detection of Malpositioned Nasogastric Tubes on Portable Supine Chest X-Rays in Intensive Care and Emergency Medicine: A Multi-center Retrospective Study.
Malposition of a nasogastric tube (NGT) can lead to severe complications. We aimed to develop a computer-aided detection (CAD) system to localize NGTs and detect NGT malposition on portable chest X-rays (CXRs). A total of 7378 portable CXRs were retrospectively retrieved from two hospitals between 2...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 1; pp. 335 - 346 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184471471&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184471471 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Feb2025 vid: 38 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184471471 184471471 184471471 10.1007/s10278-024-01181-z 184471471 ppf: 335 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning–Based Localization and Detection of Malpositioned Nasogastric Tubes on Portable Supine Chest X-Rays in Intensive Care and Emergency Medicine: A Multi-center Retrospective Study. aug: au: Wang, Chih-Hung Hwang, Tianyu Huang, Yu-Sen Tay, Joyce Wu, Cheng-Yi Wu, Meng-Che Roth, Holger R. Yang, Dong Zhao, Can Wang, Weichung Huang, Chien-Hua affil: https://ror.org/05bqach95 Department of Emergency Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan sug: subj: Deep Learning Emergency Medicine Nasoenteral Tubes Radiography, Thoracic Intensive Care Units Computer-Aided Design Supine Position Human Multicenter Studies Retrospective Design Hospitals Architecture Descriptive Statistics Confidence Intervals Taiwan Sensitivity and Specificity Academic Medical Centers Artificial Intelligence ab: Malposition of a nasogastric tube (NGT) can lead to severe complications. We aimed to develop a computer-aided detection (CAD) system to localize NGTs and detect NGT malposition on portable chest X-rays (CXRs). A total of 7378 portable CXRs were retrospectively retrieved from two hospitals between 2015 and 2020. All CXRs were annotated with pixel-level labels for NGT localization and image-level labels for NGT presence and malposition. In the CAD system, DeepLabv3 + with backbone ResNeSt50 and DenseNet121 served as the model architecture for segmentation and classification models, respectively. The CAD system was tested on images from chronologically different datasets (National Taiwan University Hospital (National Taiwan University Hospital)-20), geographically different datasets (National Taiwan University Hospital-Yunlin Branch (YB)), and the public CLiP dataset. For the segmentation model, the Dice coefficients indicated accurate delineation of the NGT course (National Taiwan University Hospital-20: 0.665, 95% confidence interval (CI) 0.630–0.696; National Taiwan University Hospital-Yunlin Branch: 0.646, 95% CI 0.614–0.678). The distance between the predicted and ground-truth NGT tips suggested accurate tip localization (National Taiwan University Hospital-20: 1.64 cm, 95% CI 0.99–2.41; National Taiwan University Hospital-Yunlin Branch: 2.83 cm, 95% CI 1.94–3.76). For the classification model, NGT presence was detected with high accuracy (area under the receiver operating characteristic curve (AUC): National Taiwan University Hospital-20: 0.998, 95% CI 0.995–1.000; National Taiwan University Hospital-Yunlin Branch: 0.998, 95% CI 0.995–1.000; CLiP dataset: 0.991, 95% CI 0.990–0.992). The CAD system also detected NGT malposition with high accuracy (AUC: National Taiwan University Hospital-20: 0.964, 95% CI 0.917–1.000; National Taiwan University Hospital-Yunlin Branch: 0.991, 95% CI 0.970–1.000) and detected abnormal nasoenteric tube positions with favorable performance (AUC: 0.839, 95% CI 0.807–0.869). The CAD system accurately localized NGTs and detected NGT malposition, demonstrating excellent potential for external generalizability. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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